Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token likelihood, semantic similarity and multi-sample consistency, remain brittle under distribution shift, domain-specialised text, and compute limits. In this work, we present Structural Confidence, a single-pass, model-agnostic framework that enhances output correctness prediction based on multi-scale structural signals derived from a model's final-layer hidden-state trajectory. By combining spectral, local-variation, and global shape descriptors, our method captures internal stability patterns that are missed by probabilities and sentence embeddings. We conduct extensive, cross-domain evaluation across four heterogeneous benchmarks-FEVER (fact verification), SciFact (scientific claims), WikiBio-hallucination (biographical consistency), and TruthfulQA (truthfulness-oriented QA). Our Structural Confidence framework demonstrates strong performance compared with established baselines in terms of AUROC and AUPR. More importantly, unlike sampling-based consistency methods which require multiple stochastic generations and an auxiliary model, our approach uses a single deterministic forward pass, offering a practical basis for efficient, robust post-hoc confidence estimation in socially impactful, resource-constrained LLM applications.
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
School of Natural and Computing Sciences University of Aberdeen（自然与计算科学学院 布拉德福德大学）；School of Computing Sciences University of East Anglia（计算科学学院 东安格利亚大学）；Siemens Energy Industrial Turbomachinery Ltd.（西门子能源工业涡轮机有限公司）；Department of Physics and Technology UiT The Arctic University of Norway（物理与技术系 UiT 北极大学）
When to Trust Context: Self-Reflective Debates for Context Reliability
Brown University（布朗大学）；Stanford University（斯坦福大学）；University of New South Wales（新南威尔士大学）；Xi’an University of Electronic Science and Technology（西安电子科技大学）；University of Chicago（芝加哥大学）
Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal
University of Liechtenstein（列支敦士登大学）；University of the Republic of San Marino（圣马尔科共和国大学）；University of Mauritius（毛里求斯大学）；International University of Monaco（摩纳哥国际大学）；University of Andorra（安道尔大学）；University of Gibraltar（直布罗陀大学）；University of the Faroe Islands（法罗群岛大学）；Ilisimatusarfik (University of Greenland)（格陵兰岛研究所（格陵兰大学））；University of Prizren（普里兹伦大学）
YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception
University of Bath（巴斯大学）；Zhejiang University（浙江大学）
Agentic AI with Orchestrator-Agent Trust: A Modular Visual Classification Framework with Trust-Aware Orchestration and RAG-Based Reasoning
University of the Peloponnese, Department of Informatics and Telecommunications（希腊皮埃蒙特大学信息与电信系）；Cornell University, Department of Biological and Environmental Engineering（康奈尔大学生物与环境工程系）
